ActBlue remains under house investigation for fraud allegations as Alexandria Ocasio-Cortez, Bernie Sanders and Abdul El-Sayed continue fundraising through the platform.

The winners of three International Joint Conferences on Artificial Intelligence (IJCAI) awards have been announced . These three distinctions are: the, the and the . The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality throughout an entire career yielding several substantial results. The winner of the 2026 Award for Research Excellence is Nicholas R. Jennings, Vice-Chancellor and President of Loughborough University, UK. Professor Jennings is recognized for his seminal contributions to the field of multi-agent systems, including algorithms for multi-agent coordination and the principles of human-agent teamwork, and for his pioneering applications of autonomous agents and multi-agent systems.

This past March, Google introduced a chatbot inside of Maps as part of its overhaul of the software's navigation suite. The company pitched the feature, Ask Maps, as a way to give users a way to obtain information no traditional map can provide. Today, Google is expanding what Ask Maps can do, starting with the addition of new agentic capabilities. The next time you feel like ordering food, you can do so directly through Ask Maps. In a press briefing, Amanda Leicht-Moore, senior product director of Google Maps, demoed the feature by telling the chatbot she wanted recommendations for avocado toast and an oat milk latte near her home.

The EurAI Distinguished Service Award started in 2012, and it is presented annually to individuals who have made exceptional contributions to the European AI community. This year, the award goes to two researchers: Jérôme Lang and Luc de Raedt. Find out who won the small, middle and large divisions in Incheon. Find out the latest from day two of the competition. In the first of our round-ups from the humanoid league we introduce the competition, and report some preliminary results.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

This paper introduces the R package spca, which provides a computational framework for least squares sparse principal component analysis (LS-SPCA). Unlike other SPCA methods, LS-SPCA generates uncorrelated sparse principal components (sPCs) that effectively maximize the explained variance while maintaining strong correlations with standard principal components (PCs). The framework also includes more computationally efficient variants that produce mildly correlated sPCs, which often have lower cardinality while explaining equal or greater variance than the LS-SPCA optimal sPCs. The spca package is built on an efficient C++ backend for matrix computations, with distinct engines for tall and fat matrices, and a flexible R frontend. The user interface offers several options for computing sPCs, such as deciding whether sparsification should stop when a threshold for cumulative variance explained or R2 with the PCs is reached, and choosing between simple forward selection, stepwise forward selection, or backward elimination for variable selection. In addition to the print(), summary(), and plot() methods, the package includes tools for comparing different "spca" solutions, grouping sparse loadings, and representing foreign SPCA solutions as "spca" objects. This article demonstrates with real datasets the use of the package in a typical LS-SPCA workflow and briefly contrasts LS-SPCA with conventional SPCA solutions . Then it compares different LS-SPCA solutions obtained from the dataset. Finally, the performance of spca on large tall and fat matrices is discussed, showing that spca offers a computationally efficient alternative for computing interpretable sPCs.

Textual data are often collected alongside structured response variables, but prediction and interpretation are commonly treated as separate tasks. This paper studies rating prediction as an initial case of interpretable text-response modelling, where the aim is to learn textual representations that are both semantically meaningful and aligned with an external response. We propose a joint non-negative matrix factorisation and binomial regression model, in which the document-topic representation is learned from both text reconstruction and rating prediction. Simulation experiments and a real-world review dataset show that the model can recover stable response-relevant textual signals and achieve competitive performance against linear and ridge regression baselines. The framework provides a practical step towards interpretable modelling of text-linked outcomes, with potential extensions to other response types beyond bounded ratings.

The essence of 3DGaussian Splatting (3DGS) training is to smartly allocate Gaussian primitives, expressing complex regions with more primitives and vice versa. Prior researches typically mark out under-reconstructed regions in a renderingloss-driven manner. However, such a loss-driven strategy is often dominated by low-frequency regions, which leads to insufficient modeling of high-frequency details in texture-rich regions. As a result, it yields a suboptimal spatial allocation of Gaussian primitives. This inspires us to excavate the loss-agnostic visual prior in training views to identify complex regions that need more primitives to model.

As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI) - what is the appropriate information to share while carrying out a certain task - becomes a central question to the field. We posit that CI demands a form of reasoning where the agent needs to reason about the context in which it is operating. To test this, we first prompt LLMs to reason explicitly about CI when deciding what information to disclose. We then extend this approach by developing a reinforcement learning (RL) framework that further instills in models the reasoning necessary to achieve CI. Using a synthetic, automatically created, dataset of only 700 examples but with diverse contexts and information disclosure norms, we show that our method substantially reduces inappropriate information disclosure while maintaining task performance across multiple model sizes and families. Importantly, improvements transfer from this synthetic dataset to established CI benchmarks such as PrivacyLens that has human annotations and evaluates privacy leakage of AI assistants in actions and tool calls. Our code is available at: https://github.com/EricGLan/CI-RL

Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data.